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All Journal Jurnal Sains dan Teknologi Sistemasi: Jurnal Sistem Informasi Jurnal CoreIT Jurnal Pengabdian Magister Pendidikan IPA Building of Informatics, Technology and Science Jurnal Manajemen Informatika dan Sistem Informasi JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Teknik Informatika (JUTIF) ABDI MASSA: Jurnal Pengabdian Nasional Indo-MathEdu Intellectuals Journal Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) Bakti Sekawan : Jurnal Pengabdian Masyarakat Jurnal Ilmiah Pengabdian dan Inovasi Kreativitas Pada Pengabdian Masyarakat (Krepa) JUAN: Jurnal Pengabdian Nusantara ECONOMIST: Jurnal Ekonomi dan Bisnis JPMI Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian kepada Masyarakat IPTEKS Jurnal Pengabdian Pada Masyarakat IPTEKS JAIM: Jurnal Aliansi Ilmu Multidisiplin
I Nyoman Switrayana
Universitas Bumigora
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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Sosialisasi Sadar Wisata dalam Mendorong Partisipasi Masyarakat untuk Pengembangan Pariwisata Berkelanjutan Roodhi, Mohammad Najib; Dakwah, Muhammad Mujahid; Abdurrahman, Abdurrahman; Muhtarom, Zamroni Alpian; Girsang, Zefanya Andryan; Bratayasa, I Wayan; Switrayana, I Nyoman; Nasri, Muhammad Haris
Bakti Sekawan : Jurnal Pengabdian Masyarakat Vol. 5 No. 1 (2025): Juni
Publisher : Puslitbang Sekawan Institute Nusa Tenggara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/bakwan.v5i1.785

Abstract

This community service activity was carried out with the main objective of increasing public awareness of the importance of tourism awareness as a foundation for achieving sustainable tourism development, particularly in the coastal area of Batu Layar, Senggigi. The activity was implemented using educational and participatory approaches involving more than 20 community partners, consisting of village officials, youth groups, MSME actors, community leaders, and local representatives. The implementation process included several key stages: delivering awareness material, conducting focused group discussions, simulating tourism service practices, and evaluating participants' understanding through pre-test and post-test assessments. The results of the activity showed a significant improvement in participants' understanding of tourism awareness concepts. This was evidenced by evaluation data, where the average pre-test score of 63.2 increased to 84.5 in the post-test. In addition to the cognitive improvements, this activity also resulted in tangible community impact in the form of collective commitments to support sustainable tourism development. Real actions taken by the community included the formation of a tourism awareness group (Pokdarwis), initiation of beach clean-up programs, and digital tourism promotion through social media platforms. This activity demonstrates that a well-designed, interactive, and stakeholder-inclusive socialization strategy can be an effective means of building collective awareness and encouraging active community participation in developing inclusive, competitive, and sustainable tourism destinations.
Sentiment Analysis and Topic Modeling of Kitabisa Applications using Support Vector Machine (SVM) and Smote-Tomek Links Methods Switrayana, I Nyoman; Ashadi, Diki; Hairani, Hairani; Aminuddin, Afrig
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 2 No. 2 (2023): September 2023
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v2i2.3406

Abstract

Kitabisa is an Indonesian application that functions to raise funds online. Users can easily support various types of campaigns and donate funds to various social causes through the app. User reviews of the application are very diverse, and it is not sure whether user reviews of the application tend to be positive, neutral, or negative. This research aimed to analyze the sentiment of the Kitabisa application by modeling topics using Latent Dirichlet Allocation (LDA) and classifying user reviews using a Support Vector Machine (SVM). The scrapped dataset showed imbalanced dataset problems, so the SMOTE-Tomek Links oversampling technique was proposed. The results of this study show that using LDA produces five topics often discussed in 750 reviews. Then, the performance of SVM without using SMOTE-Tomek Links was 72% accuracy, 76% precision, 72% recall, and 64% f1 score. Meanwhile, using SMOTE-Tomek Links could significantly improve the performance, namely 98% accuracy, 98% precision, 98% recall, and 98% f1 score. Based on this research, the application of SVM achieved high performance for user sentiment classification, especially when the dataset was in a balanced state. Therefore, the SMOTE-Tomek Links oversampling technique is recommended for dealing with unbalanced sentiment datasets.
Coaching Clinic Online: Pembuatan Artikel Ilmiah Auliana, Rini Adriani; Fitri, Yelli; Fatimah, Siti; Switrayana, I Nyoman; Talidobel, Susilo; Ramdani, Rizal; Solina, Eva
Jurnal Pengabdian Magister Pendidikan IPA Vol 8 No 4 (2025): Oktober-Desember 2025
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jpmpi.v8i4.13705

Abstract

This coaching clinic activity aims to improve and provide students with an understanding of the techniques and methods of writing scientific articles, determining research theories and appropriate research methodologies. This coaching clinic is carried out through a participatory approach and learning by doing, as well as with a learning scheme. This coaching clinic activity was conducted online through the MS Teams application platform, with 15 participants from the Accounting Department of Universitas Terbuka who came from regions throughout Indonesia. This activity was carried out four times with each meeting lasting a maximum of 120 minutes. The results of the activity were very significant, seen from the students' ability to write good scientific articles, even some scientific articles written by the training participants are planned to be published in reputable national journals. The impact of this activity was felt not only by the students who participated in the training but also by the supervising lecturers and the university.
Pengaplikasian Convolutional Neural Network (MobileNetV3) Memanfaatkan Transfer Learning Untuk Membedakan Tanaman Cabai Berasal Dari Genus Capsicum Annuum Sujaka, Tomi Tri; Switrayana, I Nyoman; Haepa Fillah, Ibnu Mumtaz
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8740

Abstract

Accurate classification of Capsicum annuum varieties is crucial for food industry applications and agricultural research. Traditional manual classification methods are time-consuming, subjective, lack detail, and are prone to human error, requiring computer vision to automate them. This study presents learning in the form of automatic classification of nine diverse Capsicum annuum varieties using transfer learning with the MobileNetV3 architecture, which is designed to achieve high accuracy and be computationally energy efficient. The dataset consists of 4,500 images (training, testing, and validation) of 9 chili varieties: bell pepper, curly chili, cherry pepper, chiltepin, Hungarian wax, jalapeno, marconi, pequin, and Thai chili. This dataset goes through quality control, one of which is dataset balancing. The model in this study has also been optimized with Adam (Adaptive Moment Estimation). Model interpretation is also improved through Grad-CAM visualization, and model robustness has also been validated using cross-validation 5 times. This model achieved performance with a training accuracy of 97.2%, a testing accuracy of 95.1%, and a validation test of 94.8%, where 5-fold cross-validation showed consistent results (94.23% ± 1.45%). Grad-CAM analysis showed that this model focuses on structural features such as shape, surface texture, and color patterns. With the successful development of an AI system that can automatically identify chili varieties with an accuracy of 95.1%. This system works well in real conditions (90.6% accuracy) and is practical for use in agriculture and food processing. This technology can help farmers and food companies or lay people to sort chilies automatically, reduce costs, and improve quality control.
A Multimodal Deep Learning Framework for Amyotrophic Lateral Sclerosis Diagnosis using Clinical and Audio Morphology Features Switrayana, I Nyoman; Sujaka, Tomi Tri; Silpiana Putri, Imelda
SISTEMASI Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i1.5763

Abstract

Amyotrophic Lateral Sclerosis (ALS) is a highly progressive neurodegenerative disease that impairs motor and speech function. Conventional diagnostic methods, both invasive and non-invasive, are often time-consuming and produce limited sensitivity. This leads to delays in treatment and worsening disease progression. This study proposes a multimodal deep learning framework that utilizes and integrates invasive medical records with non-invasive morphological features of patient speech audio extracted into Mel-Spectrograms. Unlike previous studies that focused solely on speech or clinical features, this study introduces an integrated multimodal diagnostic framework that effectively combines both data sources to achieve reliable diagnostic accuracy. The study included two experimental scenarios. In the first scenario, the audio-trained model used a Convolutional Neural Network (CNN) and was systematically optimized by testing variations in network depth, feature fusion techniques, and layer dropout probabilities to improve model generalization and stability. From the experimental results of the first scenario, the CNN achieved the best performance, achieving 80.33% accuracy in classification using audio data alone from all the tested model variations. In the second experimental scenario, when the best model was trained by incorporating clinical data, the model demonstrated improved diagnostic performance, achieving 100% accuracy. This finding highlights the importance of combining data modalities or sources from various domains, both invasive and non-invasive, to achieve optimal model performance for early ALS detection.
Co-Authors Abdurahman Abdurrahman Adinda Mutia Gani Afrig Aminuddin Apriani Apriani Arie Cahyani Ashadi, Diki Baiq Rabiatul Adawiyah Kartika Wulan Bukran Bukran Darmawan, I Made Angga Wahyu Diki Ashadi Fitri, Yelli Galuh Tinezia Endriyanto Gozin Najah Rusyada Haepa Fillah, Ibnu Mumtaz Hairani Hairani Hammad, Rifqi I Wayan Bratayasa I Wayan Mustika Nayottama Adi Wijaya Ibrahim, Isra Dewi Kuntary Indah Nalurita Kartika Gemma Pravitri Ketut Widya Kayohana Kurniadin Abd Latif Logi Mulawarman Logi Mulawarman Mohammad Ziad Anwar Muhamad Azwar, Muhamad Muhamad Wisnu Alfiansyah Muhammad Haris Nasri Muhammad Mujahid Dakwah Muhammad Nizhar Naufali Muhammad Tahir Muhammad Tahir Muhlisin Muhlisin Muhlisin Muhlisin Mulawarman, Logi Nabila, Fitri Neny Sulistianingsih Pahrul Irfan Pratama, Gede Yogi Ridha Nurul Hayati Rini Adriani Auliana Rini Anggriani Risyaf Kudus Pranasa Rizal Ramdani rodhi, mohammad najib Rusyada, Gozin Najah Silpiana Putri, Imelda Sirojul Hadi Siti Fatimah Solina, Eva Sujaka, Tomi Tri Susilo Talidobel Syafira Mahfuzi Ardiyati Syahrir, Moch. Tomi Tri Sujaka Zamroni Alpian Muhtarom Zefanya Andryan Girsang